Welcome to the InfoSci Platform
Reference Hub1
Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification

Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification

Noria Bidi, Zakaria Elberrichi
Copyright: © 2017 |Volume: 7 |Issue: 4 |Pages: 12
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781522513285|DOI: 10.4018/IJOCI.2017100103
Cite Article Cite Article

MLA

Bidi, Noria, and Zakaria Elberrichi. "Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification." IJOCI vol.7, no.4 2017: pp.51-62. http://doi.org/10.4018/IJOCI.2017100103

APA

Bidi, N. & Elberrichi, Z. (2017). Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification. International Journal of Organizational and Collective Intelligence (IJOCI), 7(4), 51-62. http://doi.org/10.4018/IJOCI.2017100103

Chicago

Bidi, Noria, and Zakaria Elberrichi. "Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification," International Journal of Organizational and Collective Intelligence (IJOCI) 7, no.4: 51-62. http://doi.org/10.4018/IJOCI.2017100103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Feature selection is essential to improve the classification effectiveness. This paper presents a new adaptive algorithm called FS-PeSOA (feature selection penguins search optimization algorithm) which is a meta-heuristic feature selection method based on “Penguins Search Optimization Algorithm” (PeSOA), it will be combined with different classifiers to find the best subset features, which achieve the highest accuracy in classification. In order to explore the feature subset candidates, the bio-inspired approach PeSOA generates during the process a trial feature subset and estimates its fitness value by using three classifiers for each case: Naive Bayes (NB), Nearest Neighbors (KNN) and Support Vector Machines (SVMs). Our proposed approach has been experimented on six well known benchmark datasets (Wisconsin Breast Cancer, Pima Diabetes, Mammographic Mass, Dermatology, Colon Tumor and Prostate Cancer data sets). Experimental results prove that the classification accuracy of FS-PeSOA is the highest and very powerful for different datasets.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.